Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays

Abstract

Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently "biorelevant," because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol's antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F₂-isoprostanes. Support vector machines, artificial neural networks, and Bayesian probabilistic learning are some key algorithms that could be deployed. Such a model will represent a robust predictive tool in assessing biorelevant antioxidant capacity of polyphenols, and thus facilitate the identification or design of antioxidant molecules. The approach will also help to fulfill the principles of the 3Rs (replacement, reduction, and refinement) in using animals in biomedical research.

Keywords: antioxidants; artificial intelligence; bioassays; machine learning; phytochemicals.

Figure 1.

Schematic illustration showing interdependence of physicochemical properties and molecular behavior as regulators of antioxidant action in a biological system, and hence required input variables in a machine learning (ML) model for predicting biorelevant antioxidant capacity.

Figure 2.

Overview of machine learning workflow for prediction of biorelevant antioxidant capacity.

Figure 3.

Oxidative stress as a key mechanism in the pathophysiology of several diseases. Measurement of F₂-isoprostanes has implicated oxidative stress in the pathophysiology of several diseases affecting many target organs. This underscores the potential of antioxidant chemical entities as adjuncts to standard treatments, chemopreventive agents for disease prevention and health promotion, and candidates for novel therapeutics (see Ref. and references cited therein).

Figure 4.

Schematic illustration of parameters (molecular descriptors) associated with each of the three dimensions governing in vivo antioxidant activity (biorelevant antioxidant action)—permeability, metabolism, and hydrogen atom transfer—with potential for use as input variables in developing a machine learning (ML)-based antioxidant capacity (AOC) assay.

Figure 5.

Supervised and unsupervised machine learning (ML) models. ML models that are applicable in quantitative structure–activity relationship (QSAR) studies consist of an array of techniques broadly classified as supervised and unsupervised learning.

Figure 6.

Chemical structures of different groups of antioxidant flavonoids (polyphenols) showing the basic structure and six major subclasses. (a) The pink oval highlights the structural similarity between flavonols and flavones (unsaturation in ring C). (b) The yellow oval highlights the structural similarity between flavanols and flavanones (saturation in ring C). (c) The green oval highlights the presence of an oxonium ion in the C ring of anthocyanins, which is responsible for their bright colors. (d) The blue oval highlights B-ring substitution at position 3 of ring C in isoflavones, instead of position 2 in flavones.